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77 results for “Vegetation modeling”
Bed morphology adjustment under the impact of a near-bank emergent model vegetation patch: implications for urban channel restoration
<p>The dataset (including videos) are uploaded to support the research study <strong>Bed morphology adjustment under the impact of a near-bank emergent model vegetation patch: implications for urban channel restoration</strong> </p>
Predicting time series of vegetation leaf area index across North America based on climate variables for land surface modeling using attention-enhanced LSTM
<p>We developed an attention-enhanced long and short memory (AELSTM) model for predicting vegetation LAI time series based on climatic data. The developed AELSTM model establishes the relationships between the time series of vegetation LAI and climatic variables. </p>
Dataset of the paper "Modeling the Flow and Geomorphic Heterogeneity Induced by Salt Marsh Vegetation Patches Based on Convolutional Neural Network UNet-Flow"
<p>Modeling the Flow and Geomorphic Heterogeneity Induced by Salt Marsh Vegetation Patches Based on Convolutional Neural Network UNet-Flow</p>
Data needed to reproduce analysis from "Frost matters: Incorporating late-spring frost in a dynamic vegetation model regulates regional productivity dynamics in European beech forests"
<p>Data to reproduce analysis from "Frost matters: Incorporating late-spring frost in a dynamic vegetation model regulates regional productivity dynamics in European beech forests".</p> <p>This includes:</p> <ol> <li>Tree ring data (meyer, bdn, principe, dittmar)</li> <li>LPJ-GUESS model output (frost_validation, frost_sensitivity, runs_22012024_revision)</li> <li>Data used for plotting</li> </ol>
Data for "Modeling the short-term fire effects on vegetation dynamics and surface energy in southern Africa"
<p>This is the data used for "Modeling the short-term fire effects on vegetation dynamics and surface energy in southern Africa using the improved SSiB4/TRIFFID-Fire model". The data includes two folders: fireon and fireoff representing the scenarios with the fire model turned on and off. Each folder includes 14 years of data from 2000-2013.</p>
Simulated dataset of dryland vegetation model
<p>This file contains simulated output from a drylands vegetation model used to produce figs 3, S3, S4 and S6 in the paper "Remotely-sensed slowing down in spatially patterned dryland ecosystems" authored by Michiel P. Veldhuis, Ricardo Martinez-Garcia, Vincent Deblauwe, Robert M. Pringle, Corina E. Tarnita & Vasilis Dakos </p>
New parameterization scheme for modeling ozone-caused damage to vegetation in process-based models: data
<p>gmd-2024-6: Quantifying the role of ozone-caused damage to vegetation in the Earth system: A new parameterization scheme for photosynthetic and stomatal responses (Fang Li et al., 2024); https://gmd.copernicus.org/preprints/gmd-2024-6/. It includes three directory: input (O3 concentration), observations (collected PODY-An and PODY-gs), and simulations.</p> <p>It is co-supported by the National Natural Science Foundation of China (41875137) and Guangdong Major Project of Basic and Applied Basic Research (2021B0301030007).</p>
In situ observations used in GMD Manuscript "A comprehensive land surface vegetation model for multi-stream data assimilation, D&B v1.0"
<p>Observations taken by</p> <p>Mika Aurela, Tarek S. El-Madany, Marika Honkanen, Anna Kontu, Juha Lemmetyinen, and Susan C. Steele-Dunne</p> <p>and used in the GMD Manuscript "A comprehensive land surface vegetation model for multi-stream data assimilation, D&B v1.0"</p>
Sensitivity of the landslide model LAPSUS_LS to vegetation and soil parameters - DATASET
<p>The dataset include the data used to run the model and perform the sensitivity of the LAPSUS_LS model to the effect of vegetation described in the paper: 'Sensitivity of the landslide model LAPSUS_LS to vegetation and soil parameters' http://dx.doi.org/10.1016/j.ecoleng.2017.08.010</p>
Mid-Holocene ENSO Variability reduced by northern African vegetation changes: a model intercomparison study
<p>This dataset contains model outputs from four climate models: EC-Earth, iCESM, UofT-CCSM4 and GISS. For each model, data is provided for three simulations: pre Industrial (PI), reference mid-Holocene without Green Sahara (MH_PMIP), and mid-Holocene with Green Sahara (MH_GS). For each simulation, data is provided for the variables: surface temperature, precipitation and zonal wind strength.</p>
Data for 'Post-disturbance recovery drives 21st century vegetation shifts in the Boreal forest in a dynamic vegetation model'
<p>This is the database containing all the LPJ-GUESS output data used in the paper. For LPJ-GUESS model code, refer to https://zenodo.org/record/8065737. For data processing refer to https://github.com/lucialayr/borealRecovery </p> <p> </p> <p>If you are interested in the raw data, please contact me. </p>
Data from: Developing state and transition models of floodplain vegetation dynamics as a tool for conservation decision-making: a case study of the Macquarie Marshes Ramsar wetland
1. Floodplain vegetation states (communities) exhibit spatiotemporal dynamics in vegetation structure and composition, which reflect unique hydrological and connectivity patterns. Shifts in inundation regimes can drive succession and establish new stable states, determined by the magnitude and duration of the hydrological perturbation. 2. We aimed to develop a modelling approach that is able to capture ecosystem dynamics, identify and quantify the main drivers of change, and provide a tool for conservation decision-making. We developed state and transition models for floodplain vegetation states based on surveys in 1991 and 2008 in the Macquarie Marshes (Australia), a Ramsar wetland of international importance. We used a Bayesian logistic regression approach to model state and transitions between vegetation states and investigated how flood frequency, distance to stream and fire frequency were associated with vegetation dynamics during this period. 3. During 1991–2008, significant transitions have occurred towards drier states. Semi-permanent wetland vegetation had the lowest persistence probability (ppsis = 0·456) and a significant threshold response of transitioning to terrestrial vegetation (ptran = 0·505). Transition to drier states was driven by lower inundation probabilities followed by increased fire probability, and distance to nearest stream. 4. Using developed models, we predicted persistence probabilities of vegetation states under an unregulated (i.e. no dams or diversions) and regulated water availability system. Under a regulated system, semi-permanent wetland vegetation had an average persistence of ppsis = 0. 67 and 0·08 in the northern and southern sections of the nature reserve, respectively. Under an unregulated system, the predicted persistence of semi-permanent wetland vegetation was considerably higher: ppsis = 0·87 and 0·38, respectively. 5. Synthesis and applications. Developing quantitative models of state transitions significantly improved our understanding of ecosystem dynamics, identifying sensitive indicators for monitoring and thus supporting conservation decision-making. This helps managers understand potential trajectories of change in ecosystems in response to management options. For example, increasing environmental flows in the Macquarie Marshes is predicted to shift the community towards more of a wetland than the terrestrial state, resulting from river regulation. State and transition models identified how key ecological assets respond to drivers of change, particularly where these can be managed. This is critical for ensuring that all ecosystem components are managed and that these do not shift into undesirable states.
Land surface modeling over the Dry Chaco: the impact of model structures, and soil, vegetation and land cover parameters
<p>The datasets archived here include simulation results shown in the paper, “Land surface modeling over the Dry Chaco: the impact of model structures, and soil, vegetation and land cover parameters”, published in Hydrology and Earth System Sciences (Maertens et al., 2021). The simulations were conducted over the South-American Dry Chaco and output on the different water budget components was produced with three land surface models (CLM2.0, CLSM-F2.5, and Noah3.6) embedded in the NASA Land Information System (LIS). The default LIS parameters were revised with (i) improved soil parameters, (ii) satellite-based interannually varying vegetation indices (leaf area index and green vegetation fraction), and (iii) yearly land cover information. For each experiment described in the manuscript, we provide daily netcdf files (0.125° resolution, period Jan 1992 – Dec 2015). The conducted experiments are simulations with (i) default LIS parameters (BL), (ii) updated soil parameters (REV<sub>s</sub>) and (iii) interannualy varying vegetation and land cover parameters (REV<sub>SV</sub>).<br> For details, please refer to</p> <p>Maertens, M., De Lannoy, G. J. M., Apers, S., Kumar, S. V., and Mahanama, S. P. P.: Land Surface modeling over the Dry Chaco: the impact of model structures, and soil, vegetation and land cover parameters, Hydrology and Earth System Sciences.</p> <p>Please contact Michiel Maertens (michiel.maertens@kuleuven.be) or Gabriëlle De Lannoy (gabrielle.delannoy@kuleuven.be) for any questions.</p>
Simulation results for "Modeling the joint effects of vegetation characteristics and soil properties on ecosystem dynamics in a Panama tropical forest"
<p>This dataset is the ELM-FATES simulation outputs for the paper entitled "Modeling the joint effects of vegetation characteristics and soil properties on ecosystem dynamics in a Panama tropical forest". </p>
Impact of updating vegetation information on land surface model performance
<p>ECLand model outputs generated and analyzed during the study "<span>Impact of updating vegetation information on land surface model performance</span>"</p>
Dataset of Flow Velocity Prediction in Vegetated Alluvial Channels Comparing Empirical and State-of-the-art Hybrid Machine Learning Models
<p>We compiled 447 datasets from different sources and lab- and field-based measurements. These datasets included Einstein and Banks (1950), Fenzl (1962), Kouwen et al. (1969), Ree and Crow (1977), Murota (1984), Tsujimoto and Kitamura (1990), Tsujimoto (1991), Tsujimoto (1993), Shimizu (1994), Dunn et al. (1996), Ikeda and Kanazawa (1996), Meijer (1998), Jarvela (2002), Rowinski and Kubrak (2002), Stone and Shen (2002), Poggi et al. (2004), Carollo et al. (2005), and Murphy et al. (2007).</p>
The Empowerment Model Towards Type 2 Diabetic Adults To Enhance Vegetable Intake in Achieving Glycemic Control
ClinicalTrials.gov study NCT01828242. IPD Sharing: Not stated. Countries: 1. Publications: 9.
Data from: New regionally modelled soil layers improve prediction of vegetation type relative to that based on global soil models
Open the record for dataset details and reuse information.
Data from: Combining optimization and simulation modelling to measure the cumulative impacts of prescribed fire and wildfire on vegetation species diversity
Open the record for dataset details and reuse information.
Data from: Developing state and transition models of floodplain vegetation dynamics as a tool for conservation decision-making: a case study of the Macquarie Marshes Ramsar wetland
Open the record for dataset details and reuse information.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.